RESULTS During euglycemia, the WMT activated the bilateral frontal and parietal cortices, insula, thalamus, and cerebellum in both
groups. During hypoglycemia, activation decreased in both groups but remained 80% larger in type 1 diabetic versus control
subjects (P < 0.05). In type 1 diabetic subjects, higher HbA1c was associated with lower activation in the right parahippocampal gyrus and amygdala (R2 = 0.45, P < 0.002). Deactivation of the default-mode network (DMN) also was seen in both groups during euglycemia. However, during
hypoglycemia, type 1 diabetic patients deactivated the DMN 70% less than control subjects (P < 0.05). Behavioral performance did not differ between glycemic conditions or groups.

CONCLUSIONS BOLD activation was increased and deactivation was decreased in type 1 diabetic versus control subjects during hypoglycemia.
This higher level of brain activation required by type 1 diabetic subjects to attain the same level of cognitive performance
as control subjects suggests reduced cerebral efficiency in type 1 diabetes.

Acute episodes of hypoglycemia are a rate-limiting adverse effect in the treatment of type 1 diabetes. When severe, they can
lead to seizures and coma (1). Even mild to moderate hypoglycemia is known to impair cognitive functions, such as working memory (2,3). Working memory is used to actively maintain and manipulate information over a brief period of time and to allocate attentional
resources among competing subtasks (4,5). Traditionally, working-memory performance is thought to depend primarily on a network of brain regions, including portions
of the frontal and parietal lobes, thalamus, precuneus, cerebellum, and insula (6,7).

Using blood oxygen level–dependent (BOLD) functional magnetic resonance imaging (fMRI), we evaluated how diabetes impacts
these neural processes under euglycemic and hypoglycemic conditions when subjects were presented with a working-memory task
(WMT). Diabetes is known to negatively affect working memory (8). This task evaluates functional effects that might reflect changes in brain structure and/or presage decreases in cognitive
performance. A better understanding of the brain’s metabolic and physiological mechanisms underlying the cognitive functions
implicated in working memory could lead to improved treatment strategies to help maintain cortical function in patients with
diabetes during hypoglycemia (9).

Whether cognitive function in patients with type 1 diabetes is affected by hypoglycemia in the same manner as in nondiabetic
individuals remains unclear because few studies using functional neural imaging have directly compared diabetic and nondiabetic
subjects during the performance of cognitive tasks (14,15). If brain glucose transport or metabolism are altered in type 1 diabetes, as has been suggested in recent studies by our
group (16) and others (17), then one would expect that the BOLD activation response during hypoglycemia may differ between diabetic patients compared
with nondiabetic control subjects. On the basis of these findings, we hypothesized that 1) patients with type 1 diabetes would have greater BOLD activation during the performance of a WMT during hypoglycemia when
compared with nondiabetic control subjects, 2) cognitive performance would deteriorate during hypoglycemia in both groups, and 3) among type 1 diabetic patients, better glycemic control (lower HbA1c) would correlate with BOLD activation responses to the WMT during hypoglycemia. We also conducted exploratory analyses to
examine deactivation patterns in the default-mode network (DMN), the regions of the brain that are more active during rest
(18), because of other research by our group examining the effects of diabetes on deactivation patterns during cognitive tasks
and previous research suggesting that DMN function may be altered in diseases that affect cognition, such as Alzheimer’s disease
(19).

RESEARCH DESIGN AND METHODS

The study sample consisted of 16 patients with type 1 diabetes and 16 healthy control subjects from an ongoing study of brain
function during hypoglycemia in type 1 diabetes. Data from five type 1 diabetic and five control subjects were included in
a previous publication (20). Demographic and clinical characteristics of the subjects are presented in Table 1. The patients’ number of self-reported hypoglycemic episodes (plasma glucose <3.9 mmol/L with concomitant symptoms) in the
month preceding the initial visit averaged eight episodes (range 2–28). Patients with autonomic neuropathy (assessed by standard
criteria ) (21), painful peripheral neuropathy, urinary albumin levels >300 mg/day, or proliferative retinopathy by review of medical records,
physical exam, or self-report were excluded from the study. Other exclusion criteria were a history of psychosis or schizophrenia;
cocaine, heroin, or alcohol dependence; and any contraindications to MRI, such as metallic implants, pregnancy, or claustrophobia.

Following approval from the institutional review boards of both the Joslin Diabetes Center (where patients were recruited)
and the McLean Hospital (where the MRI was performed), patients provided the following information during screening: self-report
of their lifetime experience of severe hypoglycemic events leading to unconsciousness (22); psychiatric history; handedness; medical history; and current medications.

Of 18 control participants who were eligible for the study, 2 were excluded from analysis because of excessive head motion
during the fMRI by applying exclusion criteria of translations in excess of 3 mm in x, y, or z directions or rotations in excess of 3° around the x, y, or z axes. No diabetic subjects were excluded for head motion.

Experimental protocol.

The experimental protocol is described elsewhere (20) and briefly reviewed below. On the day before the study, patients with type 1 diabetes had a continuous glucose monitor
(CGM System Gold; Medtronic, Northridge, CA) inserted. If the continuous glucose monitor showed glucose <3.3 mmol/L, the study
was postponed to a later date. The experiment used the insulin clamp technique with four successive time periods corresponding
to different plasma glucose levels: baseline (30 min); euglycemic clamp (40 min, target glucose 5.0 mmol/L); declining glycemia
(40 min, plasma glucose reduced from 5.0 to 2.8 mmol/L); and hypoglycemic clamp (30 min, target glucose 2.8 mmol/L). Anatomical
MRI was performed during baseline, and fMRI was performed during the euglycemic and hypoglycemic periods while the WMT was
administered (Fig. 1).

Insulin clamp technique.

An intravenous catheter was inserted into an antecubital vein for the administration of insulin and glucose, and a second
catheter was inserted into a distal forearm or hand vein for the withdrawal of blood samples. A heated gel pack was used to
warm the hand to arterialize the venous blood. After the baseline period, regular human insulin was infused at 12 pmol/kg
per min for 110 min. The plasma glucose levels were maintained at the desired level by infusion of 20% dextrose using a negative-feedback
algorithm, as previously described (23–25). After the euglycemic clamp period (40 min), the glucose infusion rate was reduced to allow the plasma glucose level to
decline by 2.2 mmol/L (from 5.0 to 2.8 mmol/L) over the next 40 min, followed by the 30-min hypoglycemic clamp period. During
the entire clamp protocol, glucose levels were measured every 5 min, and counterregulatory hormones (epinephrine, cortisol,
growth hormone, and glucagon) were measured every 10 min. At the end of the protocol, the insulin infusion was discontinued,
the glucose infusion was increased to restore euglycemia, and the subjects were given a meal and discharged.

Hormone and substrate assays.

Plasma glucose was measured using the glucose oxidase method. Serum insulin and growth hormone and plasma epinephrine were
measured by enzyme-linked immunosorbent assay. Plasma glucagon and cortisol were measured by radioimmunoassay.

WMT.

The task stimuli were administered using Presentation software (Neurobehavioral Systems, Albany, CA). The images were projected
on a backlit screen that was visualized from within the magnet bore by a mirror mounted on the head coil. Response times and
errors were collected using a magnetic resonance–compatible hand-held four-button fiberoptic response pad (FORP; Current Designs,
Philadelphia, PA) connected to the PC by an optical cable interface. The stimulus presentation was synchronized with the fMRI
acquisition sequence at the beginning of each trial by the interface that responded to scanner-generated trigger signals.
The WMT was administered 15–20 min after the beginning of the euglycemic period and again at 15–20 min after the beginning
of the hypoglycemic period (Fig. 1). Subjects performed practice tests before the study to achieve familiarity with the test procedure, and we used randomly
assigned counterbalanced forms of the test during euglycemia and hypoglycemia to minimize learning.

During the WMT, adapted from Rypma et al. (26), participants viewed a string of six digits for 1,200 ms followed by a 2,000-ms unfilled retention interval (blank screen).
Then, one digit was displayed on the computer monitor for 1,500 ms, and the subject had 1,300 ms to decide whether the digit
was a member of the previously seen string. Using the same timing parameters, a matched control task was used in which subjects
viewed six percent symbols (%) on the screen, followed by the unfilled retention interval, followed by a right- or a left-pointing
arrow. The participants were asked to press the response key corresponding to the direction of the arrow. In the rest task,
participants fixated on a plus (+) sign for 30 s. Each of these three tasks (WMT, matched control, and rest) was presented
four times in a block design for a total of 6 min of testing. There were 5 memory-scanning trials per block for a total of
20 memory-scanning trials.

Image processing.

Image processing and analyses were performed using the FSL software package (Analysis Group, FMRIB, Oxford, U.K.) running
on a Mac-Pro Quad-Core Intel-Xeon computer (Apple, Cupertino, CA). All brain images were registered to the standard MNI-152
brain (Montreal Neurologic Institute) to allow multisubject analyses in standardized space and identification of all regions
of interest. The first two EPI volumes of each functional run were discarded to allow for T1 equilibration. Prestatistical
processing of EPI time series consisted of motion correction, slice scan-time correction, nonbrain removal, spatial smoothing
with a 6-mm full-width at half-maximum three-dimensional Gaussian filter, linear trend removal, and a temporal high-pass filter
with a cutoff of 120 s. Functional data were overlaid on the MNI-152 brain.

Data analyses.

Regional brain activations and deactivations in response to the WMT were examined by performing a first-level general linear
model (GLM) multiple regression analysis for each subject and glycemic condition. Model predictors for BOLD signal time courses
were the boxcar time courses for each task stimulus paradigm convolved with a γ function to account for hemodynamic response.
Predictors for the temporal derivatives of the task paradigm were added to the design matrix to improve the fit to the data
by allowing for potential misspecification of the hemodynamic delay (27). The model for the baseline rest condition predictor was a constant function.

To compare activation and deactivation patterns between groups and conditions, higher-level multisubject mixed-effects GLM
analyses compared multiple regression correlation coefficients associated with each model predictor for each brain voxel for
each subject under each condition. Activations were computed by contrasting the WMT task to its control task. Deactivations
were computed by contrasting the WMT task to the rest period. These higher-level analyses were performed using FSL’s local
analysis of mixed effects stage 1. Resulting Z score (i.e., Gaussianized T/F) statistic images were thresholded using clusters determined by Z > 2.3 and a corrected cluster significance threshold of P = 0.05 (28). These statistical parametric maps yield the clusters of adjacent brain voxels of regional activation or deactivation from
which we computed a single brain volume of activation or deactivation at the set statistical significance threshold (P < 0.05).

To investigate the variation of the regional percent BOLD signal change, we applied a region-of-interest (ROI) analysis in
the superior parietal lobule (SPL) region, which had significant activation differences during hypoglycemia. To define the
ROI, we created a binary mask of the SPL region provided by the probabilistic Harvard-Oxford Cortical Structural Atlas (29) thresholded at the 50% probability level. The ROI analysis was performed using FSL’s FEATQUERY tool to extract the average
time courses of parameter estimates of the percent BOLD signal change from all voxels contained in the SPL mask.

To investigate the association of WMT activation and deactivation to glycemic control in type 1 diabetes, we performed the
mixed-effects GLM group analyses while including HbA1c values as a covariate in the design matrix. To compute the regional coefficients of correlation of activation to HbA1c, the average percent BOLD activation values were extracted from the regions of significant correlation in type 1 diabetes
during hypoglycemia using the FEATQUERY tool.

Standard statistical tests, including t tests for paired and unpaired data as appropriate, and ANOVA were used to compare glucose and counterregulatory hormone levels
between diabetic and nondiabetic subjects during euglycemia and hypoglycemia. All data are presented as means ± SEM, unless
otherwise specified, and statistical tests were conducted using a two-sided α level of 0.05.

RESULTS

Plasma glucose and counterregulatory hormones.

Average plasma glucose levels during the euglycemic period for nondiabetic control and type 1 diabetic subjects were 5.0 ±
0.4 mmol/L and 5.0 ± 0.6 mmol/L, respectively. During the hypoglycemic period, average glucose levels for the two groups were
2.8 ± 0.2 mmol/L and 2.7 ± 0.1 mmol/L, respectively. There was no significant difference between groups and no significant
interaction between the glycemic condition and group. Mean counterregulatory hormone levels during baseline and euglycemia
and peak levels attained during hypoglycemia are shown in Fig. 2. Glucagon and epinephrine secretion were lower in the type 1 diabetic patients during hypoglycemia compared with control
subjects.

Cognitive performance.

Type 1 diabetic and control subjects did not differ in accuracy or reaction time during the WMT at either euglycemia or hypoglycemia.
During euglycemia, control subjects achieved 85 ± 2% correct, with a reaction time of 1,133 ± 54 ms, whereas type 1 diabetic
patients had 85 ± 4% correct, with a reaction time of 1,200 ± 57 ms. At hypoglycemia, control subjects had 84 ± 5% correct,
with a reaction time of 1,122 ± 57 ms, whereas type 1 diabetic patients had 85 ± 4% correct, with a reaction time of 1,204
± 58 ms. There were no significant within-subject differences for either group when comparing the performance between glycemic
conditions and no interaction between group and condition.

Functional imaging.

Brain activations in response to the WMT for each subject group during each glycemic condition are shown in Fig. 3. The regions of greater BOLD activation for the WMT relative to the control task (P < 0.05, corrected for multiple comparisons using the cluster-based threshold method) (28) are indicated in a red-to-yellow color scale overlaid on the gray scale standard MNI-152 T1-weighted anatomical brain atlas.
For both subject groups during both glycemic conditions, the WMT activated brain regions located in bilateral medial superior
frontal gyrus, left precentral gyrus, bilateral SPL, bilateral middle frontal gyrus, bilateral anterior cingulate cortex,
bilateral insula, left supramarginal gyrus, bilateral thalamus, bilateral inferior occipital cortex, and bilateral cerebellum.

BOLD activation during the WMT in control and type 1 diabetic (T1DM) subjects during euglycemia and hypoglycemia. Statistical
parametric maps of regions of greatest activation during WMTs vs. control tasks for each subject group and glycemic condition.
Functional activations (red-to-yellow color scale) are overlaid on the MNI-152 standard brain anatomy (gray scale). The threshold
for activation was P < 0.05 after correction for multiple comparisons using the cluster-based threshold method (see research design and methods). During hypoglycemia, type 1 diabetic subjects exhibit greater activation than control subjects during the WMT. (A high-quality
digital representation of this figure is available in the online issue.)

During euglycemia, the overall extent of activation was similar in the diabetic patient and control groups, with activated
brain volumes of 613 and 498 cm3, respectively. During hypoglycemia, the extent of regional BOLD activation decreased relative to euglycemia in both subject
groups; however, it decreased less in patients with diabetes than in control subjects (P < 0.05). The regional patterns of activation also differed between groups. In type 1 diabetes, activation decreased mostly
in the insula, whereas for control subjects, activation decreased mostly in the cerebellum. Regions with higher activation
in type 1 diabetes during hypoglycemia include the bilateral SPL, bilateral posterior supramarginal gyrus, bilateral anterior
and posterior cingulate gyrus, left inferior frontal gyrus and posterior middle temporal gyrus, right hippocampus, and the
cerebellum. Thus, during hypoglycemia, activation volumes were 80% larger in type 1 diabetic versus control subjects, with
activation volumes of 431 and 239 cm3, respectively (P < 0.05). The extended activation volume in type 1 diabetic relative to control subjects during hypoglycemia was mainly a
result of maintenance of a higher percent BOLD activation during the WMT, as illustrated in Fig. 4, which compares the time courses of the percent BOLD signal estimates averaged over the SPL region for each subject group
under each glycemic condition.

Percent BOLD activation by ROI analysis during the WMT during euglycemia (left) and hypoglycemia (right). Upper: Location of the superior parietal lobule (SPL, association cortex including Brodmann areas 7 and 40) region used for ROI
analysis is indicated at the intersection of the crosshairs on the activation maps for each subject group overlaid on the
MNI-152 standard brain. Lower: Plots of the time courses of change in percent BOLD activation in the SPL ROI during the fMRI WMT. Percent change was measured
relative to BOLD activation during the control task and averaged over all pixels in the ROI and all subjects within each subject
group. The colored strip above the horizontal time axis shows the time sequence of task conditions. CONT, control task; REST,
resting state task. (A high-quality digital representation of this figure is available in the online issue.)

Regional brain deactivations in response to the WMT for each subject group during each glycemic condition are shown in Fig. 5. Regions of lesser BOLD activation (or more deactivation) for WMT relative to the resting task (P < 0.05, corrected for multiple comparisons using the cluster-based threshold method) (28) are indicated in a dark blue–to–light blue color scale overlaid on the standard MNI-152 T1-weighted anatomical brain atlas
in gray scale, along with activations in red to yellow. During euglycemia, WMT deactivated brain regions located in bilateral
medial frontal cortex, posterior cingulate cortex, and superior lateral occipital cortex for both groups. The extent of deactivation
was similar in the type 1 diabetic and control groups, with only a 10% difference between groups. Overall, deactivated brain
volumes were 45 and 41 cm3, respectively. During hypoglycemia, the extent of regional BOLD deactivation decreased relative to euglycemia in both subject
groups; however, it decreased more for patients with type 1 diabetes (P < 0.05) (i.e., they were unable to significantly deactivate these regions).

BOLD activation and deactivation during the WMT in type 1 diabetic (T1DM) and control subjects during euglycemia and hypoglycemia.
Statistical parametric maps of regions of greatest activation during WMTs vs. control tasks (functional activations in the
red-to-yellow color scale) and regions of decreased activation during the WMTs vs. resting tasks (functional deactivations
in the blue color scale) are overlaid on the MNI-152 standard brain anatomy (gray scale). The threshold for activations and
deactivations was P < 0.05 after correction for multiple comparisons using the cluster-based threshold method (see research design and methods). During hypoglycemia, patients with type 1 diabetes exhibit less deactivation than control subjects. (A high-quality digital
representation of this figure is available in the online issue.)

The regional patterns of decreased deactivation also differed between groups (Fig. 5). For patients with type 1 diabetes, deactivation decreased mostly in the medial frontal and posterior cingulate cortices,
whereas for control subjects, deactivation decreased mostly in the posterior cingulate cortex. Control subjects also had small
deactivation volumes in the cerebellum (right crus II) during hypoglycemia only. Thus, during hypoglycemia, the extent of
deactivation was almost 70% smaller in type 1 diabetic relative to control subjects, with deactivation volumes of 5 cm3 located in the superior lateral occipital cortex for patients and 17 cm3 located mainly in the medial frontal and superior lateral occipital cortices for control subjects (P < 0.05).

Finally, we examined the relationship of HbA1c levels with activation patterns in type 1 diabetes by entering HbA1c as a covariate into the GLM. We found that lower HbA1c levels were associated with higher activation in the right parahippocampal gyrus and the amygdala (Fig. 6) during hypoglycemia. We also found that HbA1c levels were not associated with deactivation.

Regions of BOLD activation inversely correlated with HbA1c in patients with type 1 diabetes. Upper: Statistical parametric map of the correlation of WMT activation with HbA1c in patients with type 1 diabetes during hypoglycemia (P < 0.05). Sagittal, coronal, and axial slices (from left to right) showing activation correlation in a red-to-yellow scale overlaid on the MNI-152 standard brain anatomy (gray scale). These
HbA1c-correlated activation clusters were identified in the right parahippocampal gyrus and amygdala using the Harvard-Oxford Cortical
Structural Atlas. Lower: Plots of regional percent BOLD activation vs. HbA1c for patients with type 1 diabetes during euglycemia (left) and hypoglycemia (right). Filled circles with error bars: average percent BOLD activation for each patient ± SD within the region of activation.
The line is the linear regression best fit of the data with its correlation coefficient R2. (A high-quality digital representation of this figure is available in the online issue.)

DISCUSSION

This study demonstrates that patients with type 1 diabetes show a different pattern of brain activation in response to a WMT
than do nondiabetic control subjects during hypoglycemia. Specifically, we found that for patients with type 1 diabetes during
hypoglycemia, WMT-related activation responses were increased in several cortical regions, including the parietal and frontal
cortices, hippocampus, and cerebellum. Task-induced deactivations, typically observed in the DMN during cognitive effort,
were significantly suppressed during hypoglycemia in bilateral medial-frontal and posterior cingulate cortices for type 1
diabetic patients compared with control subjects. Activation and deactivation patterns were similar across groups during euglycemia.
Behavioral performance on the WMT was similar across groups and conditions. Finally, HbA1c was inversely correlated with WMT activation during hypoglycemia in the right parahippocampal gyrus and amygdala, two areas
that have been reported to activate in memory-disordered populations as a form of compensatory recruitment (30–32).

The regional BOLD activations we observed in response to the WMT were compatible with those found in other fMRI studies of
similar WMTs (5,26,33–35). Although the main regions that help govern working memory are the dorsolateral and medial prefrontal cortices and anterior
cingulate cortex, other regions, such as the parietal lobe (36) and cerebellum (37), are known to play supplementary roles. These regions were activated more in type 1 diabetic patients than in control subjects
during hypoglycemia, suggesting that supplementary brain regions may have been recruited to help preserve cognitive performance.
The failure to suppress activation in the DMN also is consistent with an interpretation that type 1 diabetic patients need
to recruit more brain resources for cognitive preservation (11). Similar patterns of increased activation and decreased deactivation have been observed with mild cognitive impairment (30) and in older individuals at risk for Alzheimer’s disease (38), suggesting, in these cases, a compensatory response to accumulating pathology.

Cognitive performance was not altered by hypoglycemia in either subject group. Although some studies have shown similar results
for less challenging WMTs (39), others have shown severe impairment during hypoglycemia for highly challenging WMTs involving reasoning (2). Of importance, a number of studies have used the same Sternberg WMT used here to evaluate differences in brain activation
patterns across different populations (40,41). In our study, different brain activation patterns, despite similar cognitive performance across groups, suggest unique
strategies of brain recruitment used across groups to augment performance during the glycemic challenge.

Our results showing hyperactivation of brain regions in type 1 diabetic patients with low HbA1c and hypoactivation in patients with higher HbA1c could reflect upregulation of glucose transport in the brain, as seen in patients with good glycemic control (42,43). Type 1 diabetic patients may engage more brain regions to maintain the same performance to compensate for cerebral inefficiency
attributed to reduced brain resources (44). These results also are consistent with the patterns observed in many physiologic systems in which a period of compensatory
hyperfunction precedes functional decline and ultimate organ-system failure.

In an earlier report from our group, we demonstrated that type 1 diabetic patients showed reduced gray-matter density in the
parahippocampal gyrus associated with higher HbA1c (45). One possible explanation may be that gray-matter loss in this temporal region prevents its participation in the brain’s
response to moderate acute hypoglycemia. Wessels et al. (46) also observed abnormal brain activity patterns in patients with diabetes retinopathy, which is more likely to be associated
with elevated HbA1c levels. However, our studies differed in both design and data analytic methodology, making a direct comparison difficult.

The differences in the hypoglycemic BOLD response between patients and control subjects found in this study may be attributed
to a variety of mechanisms, including preservation of global brain glucose uptake in diabetes as a result of adaptation to
hypoglycemia (12,47), differences in neurovascular coupling, resting cerebral blood flow, neuronal activity linked to oxidative metabolism, increased
brain glycogen stores (48), or a tendency to use nonglucose substrates to support higher neuronal activity (49). In addition, changes in brain glucose transport or metabolism, resulting in increased brain glucose levels, might occur
as a result of recurrent hypoglycemia. We reported such a finding along with accompanying increases in glutamate that were
correlated with decreases in memory and executive function (16). It may follow that abnormal glutamate metabolism contributed to altered neurovascular coupling in patients in our study.
Although these adaptive mechanisms in type 1 diabetes may in the short-term allow compensation for altered brain activity
patterns during a hypoglycemic challenge, they may presage long-term maladaptive or adverse consequences.

Although our study demonstrates greater activation during hypoglycemia in type 1 diabetes along with reduced deactivation
of the DMN, the small sample size may limit the implications of our results. However, the regional BOLD activations we observed
in response to the WMT were compatible with regional activations found in other fMRI studies of similar WMTs (5,26,33–35). The reductions in BOLD response observed during hypoglycemia were also compatible with other reports showing reduced brain
BOLD activation in primary or association cortex in response to sensory, motor, or cognitive tasks in nondiabetic subjects
(11,12,14,47). Also, this study was unable to resolve whether the alterations in brain activation were secondary to chronic hyperglycemia
or recurrent hypoglycemia. Future studies can help resolve this issue.

In summary, patients with type 1 diabetes activate more brain regions than control subjects during hypoglycemia by maintaining
activity from euglycemia to hypoglycemia in task-relevant regions and by failing to suppress activation in the DMN. This suggests
that type 1 diabetic patients may need to recruit more brain resources to preserve cognitive performance. The pattern of hyperactivation
of both the DMN and task-relevant regions is consistent with findings in disease states with impaired cognition, such as mild
cognitive impairment and Alzheimer’s disease (38). There has been persistent concern about the consequences of recurrent hypoglycemia on brain structure and cognitive function.
There are minimal long-term effects of recurrent hypoglycemia on cognition into middle age (50), but it is not clear whether this resiliency will last throughout the aging process. Future research should evaluate our
findings as an early manifestation and warning of future clinically relevant cognitive decline. This research may guide the
development of treatment regimens to enhance symptom recognition or to stabilize the neurochemical response to hypoglycemia
to reduce the impact of glucodeprivation on cognitive function.

ACKNOWLEDGMENTS

This study was supported in part by National Institutes of Health grants 5R01-DK073843-03, DK-60754, DK-62218-01A1, and P30-DK-36836
(Joslin Diabetes and Endocrinology Research Center); the Herbert Graetz Fund; and grant 5M01-RR001032-32 to the Beth Israel
Deaconess General Clinical Research Center.

No potential conflicts of interest relevant to this article were reported.

N.R.B., G.M., A.M.J., and K.W. researched data, contributed to discussion, and wrote, reviewed, and edited the manuscript.
R.L.M. researched data, contributed to discussion, and reviewed and edited the manuscript. V.F. researched data and reviewed
and edited the manuscript. P.F.R. contributed to discussion and reviewed and edited the manuscript. D.C.S. researched data,
contributed to discussion, and wrote, reviewed, and edited the manuscript.

Parts of this article were presented in abstract form at the 70th Scientific Sessions of the American Diabetes Association,
Orlando Florida, 25–29 June 2010, and at the 71st Scientific Sessions of the American Diabetes Association, San Diego, California,
24–28 June 2011.

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